Deep Learning for Remote Sensing-Based Surface Soil Moisture Monitoring and Prediction: A Review

Surface soil moisture (SM) is the keystone variable of terrestrial ecohydrology. Yet, the rapid diversification and development of deep learning architectures for satellite SM estimation have outpaced practitioners’ capacity to select among them. This review synthesizes 37 deep learning studies from the SMAP era (2015–2026) across five architecture families (MLP and physics-informed neural networks [MLP/PINN], long short-term memory [LSTM] and gated recurrent unit [GRU] networks, convolutional neural networks [CNN], convolutional LSTM and graph neural networks [GNN], and Transformer-based models) to establish an architecture–task-matching framework that links each family to its dominant estimation niche. The analysis reveals consistent specializations: MLP/PINN models achieve competitive surface SM retrieval from satellite inputs; recurrent networks extend SMAP temporally (RMSE ≤ 0.035  m3 m−3); CNN disaggregates SMAP to 1 km (reported unbiased root-mean-square error (ubRMSE) approaching 0.04  m3 m−3); ConvLSTM and GNN address spatiotemporal gap-filling (low reported ubRMSE 0.022  m3 m−3); and Transformers enable global multi-source fusion and decadal climate-scenario projection. Across all families, four physics-DL integration modes (hard architectural constraints, soft loss-function penalties, physics-as-input feature engineering, and physics-ML hybrid output fusion) consistently yield RMSE reductions of 8–50% relative to data-driven baselines. These findings provide a practitioner-oriented framework that is applicable to ecohydrological monitoring of plant water stress, agricultural drought, early flood warnings, and land–atmosphere coupling.